AIARA: Artificial Intelligence Enabled Highly Adaptive Robots for Aerospace Industry

使用自适应机器人的敏捷制造提供了先进的制造，工业4.0，以实现更高效，更精益和更具成本效益的定制，大规模但小批量的产品生产。它被认为是制造业严重依赖体力劳动的最终解决方案。由于缺乏自动化，航空航天业是深受其害的行业之一。由于缺乏自动化，新飞机订单积压，航空航天业无法迅速采取行动，采用更高效的飞机设计和先进的材料。通过在制造新飞机中使用机器人来提高自动化水平，不仅迫切需要降低成本，还迫切需要提高航空航天工业的质量和安全性。然而，用于汽车工业和电子设备装配线的传统工业机器人不适合航空航天工业，因为批量小，部件大，产品多样性以及操作的高度复杂性和变化性。因此，目前在航空航天工业中，为每个特定任务编程或教机器人的做法是有限的，如果不是徒劳的话。在先进制造业和工业4.0中，机器人是智能的、高度自适应的，可以训练处理不同的设备、工具、产品和材料，而不需要明确的编程。然而，基于人工智能的学习方法需要大量的数据来捕获所有可能的物理体验来训练机器人，这可能过于昂贵或不可用。机器人技术的最新进展证明了从合成机器人的经验和模拟中学习的可行性。在提议的项目中，我们的目标是开发一种方法，利用仿真和虚拟环境的学习结果来稳健地训练多臂自适应机器人，以适应广泛的航空航天制造过程。这项研究合作伙伴关系汇集了UBC， Kinova Inc.和Element AI，这两家加拿大高科技领域的知名公司，并与德国航空航天中心DLR合作，为航空航天工业更有效的制造建立解决方案。

Agile manufacturing by the use of adaptive robots is the provision of advanced manufacturing, Industry 4.0, to enable more efficient, lean and cost-effective production of customized, large-scale but small batch size products. It is considered to be the ultimate solution for manufacturing industries hindered by their heavy reliance on manual labor. Aerospace industry is one of those industries that suffers greatly from lack of automation causing a backlog of new aircraft orders and prevents it from moving fast enough to adopt more efficient aircraft designs and advanced materials. An increased level of automation via the use of robots in manufacturing new aircrafts is pressing for not only cost reduction but also improved quality and safety in the aerospace industry. However, traditional industrial robots used in assembly lines of automotive industry and electronic devices is inadequate for the aerospace industry, because of small batch sizes, large components, diversity of products and a high level of complexity and variation in operations. Thus, the current practice of programming or teaching a robot for every specific task is limited, if not futile, in the aerospace industry. In advanced manufacturing and Industry 4.0, robots are intelligent, highly adaptive and can be trained to handle different equipment, tools, products and materials without a need for explicit programming. However, artificial intelligence-based learning methods require a large volume of data for capturing all possible physical experiences to train the robot, which can be too expensive or unavailable. Recent advances in robotics demonstrate the feasibility of learning from synthetic robot experiences and simulations. In the proposed project, we aim to develop a methodology to use learning results from simulation and virtual environments to robustly train a multi-arm adaptive robot for a wide range of aerospace manufacturing processes. This research partnership brings together the UBC, Kinova Inc., and Element AI, both prominent Canadian companies in the hi-tech sector, and in collaboration with the German Aerospace Centre DLR to build solutions for more effective manufacturing in aerospace industry.